A laser-based on-line dust detection method and system

Abstract

The present invention provides a method and system for on-line detection of dust based on laser. The method includes the following steps: when the dust concentration in the dust monitoring area is detected to be abnormal, obtain the laser scattering signal intensity, particulate matter concentration, and environmental meteorological data such as wind speed, wind direction, and humidity before and after the occurrence of the abnormality; calculate the laser scattering energy and the change rate of particulate matter concentration to obtain the dust concentration change coefficient; judge the type of abnormality according to the change coefficient and the abnormality recognition criterion. If the dust concentration in the area exceeds the standard, start the warning action to realize the management of the dust monitoring area. The present invention can accurately identify the type of dust abnormality and promptly start the warning, improving the environmental management and control capabilities of the dust monitoring area.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and more specifically, the present invention relates to a laser-based on-line dust detection method and system. Background Art

[0002] Existing dust monitoring technologies mainly rely on traditional particulate sampling and analysis methods, such as filter membrane sampling and weighing method, etc. Although these methods can provide relatively accurate particulate concentration data, they have the disadvantages of long sampling time, poor real-time performance, inability to continuously monitor, etc. In addition, traditional methods usually require manual operation, increasing the monitoring cost and workload. In recent years, with the development of laser technology, the laser scattering method has gradually been applied to the monitoring of particulate concentration. The laser scattering method calculates the particulate concentration by measuring the intensity of the laser scattering signal of the particulate matter, and has the advantages of fast response speed, wide measurement range, and continuous monitoring. However, the existing laser scattering method still has some deficiencies in dust monitoring, such as being more sensitive to the interference of environmental factors (such as wind speed, wind direction, humidity, etc.), resulting in the accuracy of the measurement results being affected; at the same time, there is a lack of an effective abnormal identification and early warning mechanism, making it difficult to detect and handle dust concentration abnormal events in a timely manner.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: it is impossible to accurately judge the type of dust concentration abnormality, it is impossible to start the early warning action in time, and it is difficult to effectively manage and control the dust monitoring area. Summary of the Invention

[0004] The present invention provides a laser-based on-line dust detection method and system.

[0005] In the first aspect of the present invention, a laser-based on-line dust detection method is provided, including:

[0006] When the dust concentration in the dust monitoring area is abnormal, obtain the laser scattering signal intensity and particulate concentration in the monitoring area before and after the abnormality occurs;

[0007] Based on the obtained information, calculate the laser scattering energy and the change rate of particulate concentration in the monitoring area, and then obtain the dust concentration change coefficient in the monitoring area;

[0008] Wherein, the calculation formula of the laser scattering energy is:

[0009]

[0010] In the formula, is the laser scattering signal intensity changing with time in the monitoring area, is the monitoring period;

[0011] According to the dust concentration change coefficient and the dust concentration anomaly identification criterion in the monitoring area, judge the type of dust concentration anomaly. If the anomaly type is that the dust concentration in the area exceeds the standard, start the warning action to realize the management of the dust monitoring area.

[0012] Further, the dust concentration change coefficient in the monitoring area is expressed as:

[0013]

[0014] In the formula, represents the dust concentration change coefficient; , respectively represent the laser scattering signal intensities before and after the anomaly occurs; , respectively represent the particulate matter concentrations before and after the anomaly occurs.

[0015] Further, the dust concentration anomaly identification criterion includes:

[0016] If it is satisfied in the monitoring area that the dust concentration change coefficient is greater than the first set threshold and the particulate matter concentration change rate is greater than the second set threshold, it is judged as an event that the dust concentration in the area exceeds the standard;

[0017] If it is satisfied in the monitoring area that the dust concentration change coefficient is less than or equal to the first set threshold, but the particulate matter concentration change rate is greater than the second set threshold, it is judged as a dust diffusion event;

[0018] If it is satisfied in the monitoring area that the dust concentration change coefficient is greater than the first set threshold, but the particulate matter concentration change rate is less than or equal to the second set threshold, it is judged as a dust source release event;

[0019] If it is simultaneously satisfied in the monitoring area that the dust concentration change coefficient is less than or equal to the first set threshold and the particulate matter concentration change rate is less than or equal to the second set threshold, it is judged as a normal environmental fluctuation.

[0020] Further, the first set threshold and the second set threshold in the monitoring area are determined by the following method:

[0021] The first set threshold = ;

[0022] The second set threshold = ;

[0023] In the formula, represents the average dust concentration change coefficient of the monitoring area under normal environmental conditions, represents the maximum dust concentration change coefficient in the monitoring area under extreme environmental conditions; represents the average particulate matter concentration change rate in the monitoring area under normal environmental conditions, represents the maximum particulate matter concentration change rate in the monitoring area under extreme environmental conditions;

[0024] The , , , acquisition method is:

[0025] Through statistical analysis of historical monitoring data, calculate the average and maximum dust concentration change coefficients and particulate matter concentration change rates in the monitoring area under normal and extreme environmental conditions respectively.

[0026] Furthermore, the maximum dust concentration change coefficient in the monitoring area under extreme environmental conditions is expressed as:

[0027]

[0028] In the formula, , respectively represent the maximum and minimum laser scattering signal intensities in the monitoring area under extreme environmental conditions; , respectively represent the maximum and minimum particulate matter concentrations in the monitoring area under extreme environmental conditions.

[0029] Furthermore, the maximum laser scattering signal intensity in the monitoring area is expressed as:

[0030]

[0031] In the formula, is the laser scattering signal intensity changing with time in the monitoring area, is the Dirac function, is the time point when the laser scattering signal intensity reaches the maximum value under extreme environmental conditions, is the monitoring period.

[0032] Furthermore, the maximum particulate matter concentration in the monitoring area is expressed as:

[0033]

[0034] In the formula, is the particulate matter concentration changing with time in the monitoring area, is the Dirac function, is the time point when the particulate matter concentration reaches the maximum value under extreme environmental conditions, is the monitoring period.

[0035] Furthermore, the rate of change of the particulate matter concentration in the monitoring area is expressed as:

[0036]

[0037] In the formula, is the particulate matter concentration at the current moment in the monitoring area, is the particulate matter concentration at the next moment in the monitoring area, is the time interval.

[0038] In the second aspect of the present invention, an online dust detection system based on laser is provided, including:

[0039] A data acquisition module, configured to obtain the laser scattering signal intensity and particulate matter concentration in the monitoring area before and after the anomaly occurs when the dust concentration in the dust monitoring area is detected to be abnormal;

[0040] A laser scattering energy calculation module, configured to calculate the laser scattering energy according to the obtained laser scattering signal intensity, and the calculation formula is:

[0041]

[0042] In the formula, is the laser scattering signal intensity changing with time in the monitoring area, is the monitoring period;

[0043] A dust concentration change coefficient calculation module, configured to calculate the laser scattering energy and the particulate matter concentration change rate in the monitoring area based on the obtained information, and further obtain the dust concentration change coefficient in the monitoring area;

[0044] A dust concentration anomaly identification and early warning action module, configured to judge the dust concentration anomaly type according to the dust concentration change coefficient and the dust concentration anomaly identification criterion in the monitoring area. If the anomaly type is that the dust concentration in the area exceeds the standard, start the early warning action to realize the management of the dust monitoring area.

[0045] Furthermore, the dust concentration change coefficient in the monitoring area in the dust concentration anomaly identification and early warning action module is expressed as:

[0046]

[0047] In the formula, represents the dust concentration change coefficient; , respectively represent the laser scattering signal intensity before and after the anomaly occurs; , respectively represent the particulate matter concentration before and after the occurrence of the anomaly.

[0048] The above embodiments of the present invention have at least the following beneficial effects: By continuously monitoring the laser scattering signal intensity and particulate matter concentration, and combining environmental meteorological data such as wind speed, wind direction, and humidity, the present invention can accurately calculate the change coefficient of the dust concentration in the monitoring area. This method can effectively identify abnormal changes in the dust concentration, such as dust concentration exceeding the standard, dust diffusion events, and dust source release events, thereby improving the accuracy and reliability of monitoring. In addition, this method can also promptly initiate warning actions according to the abnormal identification criterion, realizing dynamic management and control of the dust monitoring area, helping to take timely measures to deal with dust pollution events, and reducing the impact on the environment and human health.

[0049] At the same time, the present invention adopts the laser scattering technology, which has the advantages of fast response speed, wide measurement range, and continuous monitoring, and can realize real-time monitoring and rapid response of the dust concentration. Compared with the traditional particulate matter sampling and analysis methods, this method greatly improves the monitoring efficiency, reduces the monitoring cost and workload, is applicable to large-scale dust monitoring applications, and has a wide application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of illustration and not limitation, in which:

[0051] Figure 1 is a schematic flow chart of an on-line dust detection method based on laser provided by an embodiment of the present invention;

[0052] Figure 2 is a schematic structural diagram of an on-line dust detection system based on laser provided by an embodiment of the present invention;

[0053] Figure 3 schematically shows a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and do not limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to convey the scope of the present invention to those skilled in the art in a complete manner.

[0055] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, equipment, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0056] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0057] The following refers to Figure 1 , Figure 1 which is a schematic flow chart of a laser-based on-line dust detection method provided for an embodiment of the present invention. As Figure 1 shown, a laser-based on-line dust detection method 100 includes:

[0058] Step 101, when the dust concentration in the dust monitoring area is abnormal, obtain the laser scattering signal intensity, particulate matter concentration, and environmental meteorological data such as wind speed, wind direction, and humidity in the monitoring area before and after the abnormality occurs;

[0059] Step 102, based on the obtained information, calculate the laser scattering energy and the change rate of particulate matter concentration in the monitoring area, and then obtain the dust concentration change coefficient in the monitoring area;

[0060] Among them, the calculation formula for laser scattering energy is:

[0061]

[0062] In the formula, is the laser scattering signal intensity that changes with time in the monitoring area, is the monitoring period;

[0063] Step 103, according to the dust concentration change coefficient and the dust concentration abnormality recognition criterion in the monitoring area, judge the type of dust concentration abnormality. If the abnormal type is that the dust concentration in the area exceeds the standard, start the warning action to realize the management of the dust monitoring area.

[0064] It should be noted that the on-line dust detection method of the present invention determines whether the dust concentration is abnormal by monitoring the changes in the laser scattering signal intensity and the particulate matter concentration. The laser scattering signal intensity refers to the intensity of the optical signal scattered after the interaction between the laser and the particulate matter in the monitoring area, and the particulate matter concentration refers to the mass or quantity of particulate matter per unit volume of air. When an abnormal dust concentration is detected, the system will obtain relevant data before and after the occurrence of the abnormality, including the laser scattering signal intensity, the particulate matter concentration, and environmental meteorological data such as wind speed, wind direction, and humidity. The acquisition of these data is to more accurately analyze the changes in the dust concentration, so as to determine the type of abnormality and take corresponding warning measures.

[0065] Specifically, the calculation formula for the laser scattering energy is , where is the laser scattering signal intensity that changes with time in the monitoring area, is the monitoring period. The monitoring period can be set according to the actual monitoring requirements. For example, it can be set from a few minutes to several hours to meet the requirements of different monitoring scenarios. The calculation formula for the change rate of the particulate matter concentration is

[0066]

[0067] where, is the particulate matter concentration at the current moment, is the particulate matter concentration at the next moment, is the time interval. The time interval can be set according to the sampling frequency of the monitoring device, usually from a few seconds to a few minutes.

[0068] More specifically, the calculation formula for the dust concentration change coefficient is

[0069]

[0070] where, and respectively represent the laser scattering signal intensities before and after the occurrence of the abnormality, and respectively represent the particulate matter concentrations before and after the occurrence of the abnormality. The settings of these parameters can be adjusted according to the specific monitoring environment and equipment performance to achieve the best monitoring effect.

[0071] Preferably, in order to improve the accuracy of the monitoring, multiple monitoring points can be set in the monitoring area to obtain the laser scattering signal intensity and particulate matter concentration data at different positions respectively. By comparing the data of different monitoring points, the changes in the dust concentration and the type of abnormality can be judged more accurately.

[0072] Furthermore, other environmental monitoring data, such as temperature and air pressure, can be combined to further analyze the possible causes of the change in dust concentration. For example, when a sudden increase in particulate matter concentration is detected, by analyzing the wind speed and wind direction data, it can be determined whether it is a dust diffusion event or a dust source release event. If the wind speed is relatively high and the wind direction points to the monitoring area, it may be a dust diffusion event; if the wind speed is relatively low and there is an obvious dust source within the monitoring area, it may be a dust source release event. Through these refined operation steps and alternative solutions, the practicability and reliability of the on-line dust detection method can be further improved.

[0073] In some embodiments, the dust concentration change coefficient in the monitoring area is expressed as:

[0074]

[0075] In the formula, represents the dust concentration change coefficient; , respectively represent the laser scattering signal intensities before and after the abnormality occurs; , respectively represent the particulate matter concentrations before and after the abnormality occurs.

[0076] It should be noted that the dust concentration change coefficient is an important indicator to measure the degree of change in dust concentration in the monitoring area. It is calculated by comparing the changes in laser scattering signal intensity and particulate matter concentration before and after the abnormality occurs. The laser scattering signal intensity reflects the scattering ability of particulate matter to laser, while the particulate matter concentration directly represents the content of particulate matter per unit volume. By calculating the change rates of these two parameters, the change situation of dust concentration can be evaluated more accurately, thus providing reliable data support for dust monitoring and management.

[0077] Specifically, the calculation formula of the dust concentration change coefficient is

[0078]

[0079] Wherein, and respectively represent the laser scattering signal intensities before and after the abnormality occurs, and respectively represent the particulate matter concentrations before and after the abnormality occurs. In practical applications, the laser scattering signal intensity can be collected in real time by a laser sensor, and the particulate matter concentration can be indirectly measured by a particulate matter sensor or the laser scattering method. For example, when it is monitored that the laser scattering signal intensity increases from 1000 units to 1200 units and the particulate matter concentration increases from 50 μg / m³ to 60 μg / m³, the dust concentration change coefficient can be calculated as This value can be used to judge the degree of change in the dust concentration and the possible types of anomalies.

[0080] Preferably, in order to improve the calculation accuracy of the dust concentration change coefficient, a multi-point averaging method can be adopted. That is, multiple monitoring points are set in the monitoring area, the laser scattering signal intensity and particulate matter concentration data of each point are collected respectively, then the dust concentration change coefficient of each point is calculated, and finally the average value is taken as the dust concentration change coefficient of the entire monitoring area.

[0081] Furthermore, the concept of time weighting can also be considered, and the change coefficient is weighted according to the time interval before and after the occurrence of the anomaly. For example, if the time interval before and after the occurrence of the anomaly is short, a larger weight can be given to highlight the rapid change in the dust concentration within a short period of time. Through these refined operation steps and alternative solutions, the calculation accuracy and reliability of the dust concentration change coefficient can be further improved, providing more reliable data support for dust monitoring and management.

[0082] In some embodiments, the dust concentration anomaly identification criterion includes:

[0083] If in the monitoring area, the dust concentration change coefficient is greater than the first set threshold and the particulate matter concentration change rate is greater than the second set threshold, it is judged as an event of excessive dust concentration in the area;

[0084] If in the monitoring area, the dust concentration change coefficient is less than or equal to the first set threshold, but the particulate matter concentration change rate is greater than the second set threshold, it is judged as a dust diffusion event;

[0085] If in the monitoring area, the dust concentration change coefficient is greater than the first set threshold, but the particulate matter concentration change rate is less than or equal to the second set threshold, it is judged as a dust source release event;

[0086] If in the monitoring area, the dust concentration change coefficient is less than or equal to the first set threshold and the particulate matter concentration change rate is less than or equal to the second set threshold at the same time, it is judged as a normal environmental fluctuation.

[0087] It should be noted that the dust concentration anomaly identification criterion is a standard for judging whether the change in the dust concentration in the monitoring area belongs to an abnormal situation. By setting thresholds, normal environmental fluctuations and abnormal events can be distinguished, so as to realize the effective monitoring and early warning of dust pollution. The first set threshold and the second set threshold are respectively used to judge whether the dust concentration change coefficient and the particulate matter concentration change rate reach the abnormal standard. For example, when the dust concentration change coefficient is greater than the first set threshold, it indicates that the degree of change in the dust concentration is large and may belong to an abnormal event; when the particulate matter concentration change rate is greater than the second set threshold, it indicates that the change speed of the particulate matter concentration is fast and may also belong to an abnormal event.

[0088] Specifically, the determination of the first set threshold and the second set threshold requires analysis based on the historical data and environmental conditions of the monitoring area. The first set threshold can be set as a certain multiple of the average dust concentration change coefficient of the monitoring area under normal environmental conditions, such as 1.5 times or 2 times. The second set threshold can be set as a certain multiple of the average particulate matter concentration change rate of the monitoring area under normal environmental conditions, such as 2 times or 3 times.

[0089] More specifically, the threshold can also be adjusted and optimized according to the specific situation of the monitoring area, combined with expert experience and on-site investigation results. For example, in areas with relatively serious dust pollution such as industrial areas or construction sites, the threshold can be appropriately increased to avoid frequent false alarms; in areas with high air quality requirements such as residential areas or nature reserves, the threshold can be appropriately decreased to improve the sensitivity of monitoring.

[0090] Preferably, in order to improve the accuracy of anomaly recognition, a method of dynamically adjusting the threshold can be adopted. That is, the first set threshold and the second set threshold are dynamically adjusted according to the real-time environmental conditions and historical monitoring data of the monitoring area. For example, when it is detected that the wind speed is relatively high or the humidity is relatively high, the threshold can be appropriately increased to reduce the interference of environmental factors on the monitoring results; when it is detected that the air quality is relatively poor or there are obvious dust sources, the threshold can be appropriately decreased to promptly discover and handle abnormal events.

[0091] Furthermore, machine learning algorithms can be introduced to automatically identify abnormal events through training models, further improving the intelligence level of monitoring. Through these refined operation steps and alternative solutions, it is possible to better meet the monitoring requirements under different environmental conditions and improve the accuracy and reliability of dust concentration anomaly recognition.

[0092] In some embodiments, the first set threshold and the second set threshold within the monitoring area are determined in the following manner:

[0093] The first set threshold = ;

[0094] The second set threshold = ;

[0095] In the formula, represents the average dust concentration change coefficient of the monitoring area under normal environmental conditions, represents the maximum dust concentration change coefficient of the monitoring area under extreme environmental conditions; represents the average particulate matter concentration change rate of the monitoring area under normal environmental conditions, represents the maximum particulate matter concentration change rate of the monitoring area under extreme environmental conditions;

[0096] The , , , acquisition method is as follows:

[0097] By statistically analyzing historical monitoring data, calculate the average and maximum dust concentration change coefficients and particulate matter concentration change rates in the monitoring area under normal and extreme environmental conditions respectively.

[0098] It should be noted that the first set threshold and the second set threshold are key parameters for judging whether the dust concentration change coefficient and the particulate matter concentration change rate are abnormal. The first set threshold is determined based on the average and maximum dust concentration change coefficients in the monitoring area under normal and extreme environmental conditions, while the second set threshold is determined based on the average and maximum particulate matter concentration change rates. The setting of these thresholds is to ensure that the monitoring system can accurately identify abnormal changes in dust concentration under different environmental conditions, so as to take corresponding early warning measures in a timely manner.

[0099] Specifically, the first set threshold can be determined by calculating the average dust concentration change coefficient in the monitoring area under normal environmental conditions and the maximum dust concentration change coefficient under extreme environmental conditions , and the formula is

[0100]

[0101] Similarly, the second set threshold can be determined by calculating the average particulate matter concentration change rate under normal environmental conditions and the maximum particulate matter concentration change rate under extreme environmental conditions , and the formula is

[0102]

[0103] These parameters can be obtained through the statistical analysis of historical monitoring data. For example, by collecting monitoring data for one year or longer, calculate the average and maximum values under different environmental conditions.

[0104] Preferably, in order to improve the accuracy and adaptability of threshold setting, a dynamic adjustment mechanism can be adopted. For example, according to seasonal changes or weather forecast information, dynamically adjust the first set threshold and the second set threshold. In the sandstorm season or extreme weather conditions, the threshold can be appropriately increased to reduce false alarms; in seasons or weather conditions with good air quality, the threshold can be appropriately reduced to improve the sensitivity of monitoring.

[0105] Furthermore, an adaptive algorithm can be introduced to automatically adjust the threshold according to real-time monitoring data and changes in environmental conditions, enabling the monitoring system to better adapt to different monitoring environments. Through these refined operation steps and alternative solutions, the accuracy and reliability of the monitoring system can be further improved, ensuring the timely and effective identification and early warning of dust concentration abnormal events.

[0106] In some embodiments, the maximum dust concentration change coefficient in the monitoring area under extreme environmental conditions is expressed as:

[0107]

[0108] In the formula, and respectively represent the maximum and minimum laser scattering signal intensities in the monitoring area under extreme environmental conditions; and respectively represent the maximum and minimum particulate matter concentrations in the monitoring area under extreme environmental conditions.

[0109] It should be noted that the maximum dust concentration change coefficient under extreme environmental conditions is an important parameter for evaluating the degree of dust concentration change in the monitoring area under the most adverse environmental conditions. Extreme environmental conditions usually refer to situations such as extremely high wind speed, extremely low humidity, or extremely high particulate matter concentration, which may cause drastic changes in dust concentration. The maximum dust concentration change coefficient is calculated by comparing the maximum and minimum laser scattering signal intensities and particulate matter concentrations under extreme environmental conditions, and can effectively reflect the maximum change range of dust concentration under extreme conditions, providing a basis for setting reasonable monitoring thresholds.

[0110] Specifically, the calculation formula for the maximum dust concentration change coefficient under extreme environmental conditions is

[0111]

[0112] where and respectively represent the maximum and minimum laser scattering signal intensities in the monitoring area under extreme environmental conditions, and respectively represent the maximum and minimum particulate matter concentrations in the monitoring area under extreme environmental conditions.

[0113] More specifically, in practical applications, these parameters can be obtained through data records of the monitoring equipment under extreme environmental conditions. For example, during the sandstorm season, the maximum laser scattering signal intensity recorded by the monitoring equipment is 2000 units, the minimum is 500 units, the maximum particulate matter concentration is 100 micrograms per cubic meter, and the minimum is 20 micrograms per cubic meter, then the maximum dust concentration change coefficient is This value can be used to evaluate the change in dust concentration in the monitoring area under extreme conditions.

[0114] Preferably, in order to improve the calculation accuracy and reliability of the maximum dust concentration change coefficient, a multi-point monitoring and data fusion method can be adopted. Multiple monitoring points are set in the monitoring area to collect the laser scattering signal intensity and particulate matter concentration data under extreme environmental conditions respectively, and then the data of each point are fused and processed to calculate the maximum dust concentration change coefficient of the entire monitoring area.

[0115] Furthermore, meteorological data and geographical information can be combined to make more accurate predictions and judgments on extreme environmental conditions, thereby improving the accuracy and reliability of monitoring results. For example, through the wind speed and humidity data provided by the meteorological station, the occurrence time and duration of extreme environmental conditions can be judged more accurately, and then the calculation accuracy of the maximum dust concentration change coefficient can be improved.

[0116] In some embodiments, the maximum laser scattering signal intensity in the monitoring area , is expressed as:

[0117]

[0118] In the formula, is the laser scattering signal intensity that changes with time in the monitoring area, is the Dirac function, is the time point when the laser scattering signal intensity reaches the maximum under extreme environmental conditions, is the monitoring period.

[0119] It should be noted that the maximum laser scattering signal intensity refers to the maximum value reached by the laser scattering signal intensity in the monitoring area under extreme environmental conditions. It is one of the important parameters for evaluating the change in particulate matter concentration in the monitoring area under extreme conditions. By calculating the maximum laser scattering signal intensity, the scattering ability of particulate matter to laser under extreme environmental conditions can be understood more accurately, thereby providing reliable data support for monitoring and early warning. Extreme environmental conditions usually include high wind speed, low humidity or high particulate matter concentration, etc., which may lead to drastic changes in particulate matter concentration and thus affect the intensity of the laser scattering signal.

[0120] Specifically, the calculation formula for the maximum laser scattering signal intensity is

[0121]

[0122] Among them, is the laser scattering signal intensity that changes with time in the monitoring area, is the Dirac function, is the time point when the laser scattering signal intensity reaches the maximum under extreme environmental conditions, is the monitoring period.

[0123] More specifically, in practical applications, the laser scattering signal intensity can be collected in real time by a laser sensor, and the monitoring period can be set according to specific monitoring requirements. For example, it can be set from a few minutes to several hours. For example, when the monitoring period is 1 hour, if the laser scattering signal intensity recorded by the laser sensor reaches the maximum value of 2000 units at a certain moment, then the maximum laser scattering signal intensity at this moment is 2000 units.

[0124] Preferably, in order to improve the calculation accuracy and reliability of the maximum laser scattering signal intensity, data smoothing and filtering methods can be used to preprocess the collected laser scattering signal intensity data. For example, a moving average filter or a high-pass filter can be used to remove noise and outliers, so as to obtain more stable and accurate maximum laser scattering signal intensity data.

[0125] Furthermore, other environmental monitoring data, such as wind speed and humidity, can also be combined to make a more accurate judgment and prediction of extreme environmental conditions, thereby improving the accuracy and reliability of the calculation results. For example, when it is monitored that the wind speed suddenly increases and the humidity decreases, it can be judged that extreme environmental conditions have occurred, and then the maximum laser scattering signal intensity can be calculated more accurately.

[0126] In some embodiments, the maximum particulate matter concentration in the monitoring area is expressed as:

[0127]

[0128] In the formula, is the particulate matter concentration that changes with time in the monitoring area, is the Dirac function, is the time point when the particulate matter concentration reaches the maximum under extreme environmental conditions, is the monitoring period.

[0129] It should be noted that the maximum particulate matter concentration refers to the maximum value of the particulate matter concentration in the monitoring area under extreme environmental conditions. It is one of the important indicators for evaluating the particulate matter pollution degree in the monitoring area under extreme conditions. By calculating the maximum particulate matter concentration, the distribution and change of particulate matter under extreme environmental conditions can be understood more accurately, thus providing reliable data support for monitoring and early warning. Extreme environmental conditions usually include high wind speed, low humidity or high particulate matter emissions, etc. These conditions may cause drastic changes in particulate matter concentration, thereby affecting air quality.

[0130] Specifically, the calculation formula for the maximum particulate matter concentration is

[0131]

[0132] where is the particulate matter concentration changing with time in the monitoring area, is the Dirac function, is the time point when the particulate matter concentration reaches the maximum value under extreme environmental conditions, is the monitoring period.

[0133] More specifically, in practical applications, the particulate matter concentration can be collected in real time by a particulate matter sensor, and the monitoring period can be set according to specific monitoring requirements. For example, it can be set from a few minutes to several hours. For example, when the monitoring period is 1 hour and the particulate matter concentration recorded by the particulate matter sensor reaches the maximum value of 100 μg / m³ at a certain moment, the maximum particulate matter concentration at that moment is 100 μg / m³.

[0134] Preferably, in order to improve the calculation accuracy and reliability of the maximum particulate matter concentration, data smoothing and filtering methods can be used to preprocess the collected particulate matter concentration data. For example, a moving average filter or a high-pass filter can be used to remove noise and outliers, so as to obtain more stable and accurate maximum particulate matter concentration data.

[0135] Furthermore, other environmental monitoring data, such as wind speed and humidity, can be combined to make a more accurate judgment and prediction of extreme environmental conditions, thereby improving the accuracy and reliability of the calculation results. For example, when it is monitored that the wind speed suddenly increases and the humidity decreases, it can be judged that extreme environmental conditions occur, and then the maximum particulate matter concentration can be calculated more accurately.

[0136] In some embodiments, the change rate of the particulate matter concentration in the monitoring area , is expressed as:

[0137]

[0138] In the formula, is the particulate matter concentration at the current moment in the monitoring area, is the particulate matter concentration at the next moment in the monitoring area, is the time interval.

[0139] It should be noted that the particulate matter concentration change rate is an important indicator for measuring the change speed of particulate matter concentration over time in the monitoring area. It is obtained by calculating the ratio of the difference between the particulate matter concentrations at the current moment and the next moment to the time interval. The calculation of the particulate matter concentration change rate can help the monitoring system promptly detect rapid changes in particulate matter concentration, thereby determining whether abnormal events such as dust diffusion or dust source release occur, and providing a basis for early warning and management.

[0140] Specifically, the calculation formula for the particulate matter concentration change rate is

[0141]

[0142] Wherein, is the particulate matter concentration at the current moment, is the particulate matter concentration at the next moment, is the time interval.

[0143] More specifically, in practical applications, the particulate matter concentration can be collected in real time by a particulate matter sensor, and the time interval can be set according to the sampling frequency of the monitoring device, usually ranging from several seconds to several minutes. For example, if the particulate matter concentration is 50 micrograms per cubic meter at a certain moment and 60 micrograms per cubic meter at the next moment (with an interval of 1 minute), then the particulate matter concentration change rate is micrograms per cubic meter per minute.

[0144] Preferably, in order to improve the calculation accuracy and reliability of the particulate matter concentration change rate, data smoothing and filtering methods can be used to preprocess the collected particulate matter concentration data. For example, a moving average filter or a high-pass filter can be used to remove noise and outliers, thereby obtaining more stable and accurate particulate matter concentration change rate data.

[0145] Furthermore, other environmental monitoring data, such as wind speed and humidity, can also be combined to conduct a more comprehensive analysis of the change in particulate matter concentration, thereby improving the accuracy and reliability of the monitoring results. For example, when it is monitored that the wind speed suddenly increases and the particulate matter concentration rapidly rises, it can be determined that a dust diffusion event has occurred, and then the particulate matter concentration change rate can be calculated more accurately.

[0146] The above-mentioned various embodiments of the present invention have the following beneficial effects: The method and system of the present invention can achieve precise monitoring and analysis of the change in dust concentration by comprehensively analyzing the laser scattering signal intensity, particulate matter concentration, and environmental meteorological data. This method uses the laser scattering energy and the change rate of particulate matter concentration to calculate the dust concentration change coefficient, which can effectively identify different types of dust anomaly events, such as dust concentration exceeding the standard, dust diffusion, and dust source release. This method not only improves the accuracy and reliability of monitoring, but also can initiate warning actions in a timely manner according to the anomaly identification criterion, realizing the dynamic management and control of the dust monitoring area, helping to take measures in a timely manner to deal with dust pollution events and reducing the impact on the environment and human health.

[0147] In addition, this method uses laser scattering technology, which has the advantages of fast response speed, wide measurement range, and continuous monitoring, and can achieve real-time monitoring and rapid response to dust concentration. Compared with traditional particulate matter sampling and analysis methods, this method greatly improves the monitoring efficiency, reduces the monitoring cost and workload, and is suitable for large-scale dust monitoring applications. The threshold determined by statistical analysis of historical monitoring data further enhances the adaptability and stability of the system, enabling it to operate stably under different environmental conditions and having a wide range of application prospects.

[0148] As Figure 2 shown, an on-line dust detection system 200 based on laser in some embodiments, the system 200 includes:

[0149] A data acquisition module 201, configured to obtain the laser scattering signal intensity, particulate matter concentration, and environmental meteorological data such as wind speed, wind direction, and humidity in the monitoring area before and after the anomaly occurs when it is detected that the dust concentration in the dust monitoring area is abnormal;

[0150] A laser scattering energy calculation module 202, configured to calculate the laser scattering energy according to the obtained laser scattering signal intensity, and the calculation formula is:

[0151]

[0152] In the formula, is the laser scattering signal intensity changing with time in the monitoring area, is the monitoring period;

[0153] A dust concentration change coefficient calculation module 203, configured to calculate the change rate of the laser scattering energy and the particulate matter concentration in the monitoring area based on the obtained information, and then obtain the dust concentration change coefficient in the monitoring area;

[0154] The dust concentration anomaly identification and early warning action module 204 is used to determine the type of dust concentration anomaly according to the dust concentration change coefficient and the dust concentration anomaly identification criterion in the monitoring area. If the anomaly type is that the dust concentration in the area exceeds the standard, an early warning action is initiated to realize the management of the dust monitoring area.

[0155] It can be understood that the various modules described in the laser-based on-line dust detection system 200 correspond to the respective steps in the laser-based on-line dust detection method described in the reference Figure 1 description. Therefore, the operations, features, and beneficial effects described above for the laser-based on-line dust detection method also apply to the laser-based on-line dust detection system 200 and the modules included therein, and will not be elaborated here.

[0156] In some embodiments, the dust concentration change coefficient in the monitoring area in the dust concentration anomaly identification and early warning action module is expressed as:

[0157]

[0158] In the formula, represents the dust concentration change coefficient; , respectively represent the laser scattering signal intensities before and after the anomaly occurs; , respectively represent the particulate matter concentrations before and after the anomaly occurs.

[0159] It should be noted that the dust concentration change coefficient is an important indicator for evaluating the change degree of particulate matter concentration in the monitoring area. It is calculated by comparing the laser scattering signal intensities and particulate matter concentrations before and after the anomaly occurs. The laser scattering signal intensity reflects the scattering ability of particulate matter to laser, while the particulate matter concentration directly represents the particulate matter content per unit volume. By calculating the change rates of these two parameters, the change situation of dust concentration can be evaluated more accurately, thus providing reliable data support for dust monitoring and management.

[0160] Specifically, the calculation formula of the dust concentration change coefficient is

[0161]

[0162] Among them, and respectively represent the laser scattering signal intensities before and after the anomaly occurs, and respectively represent the particulate matter concentrations before and after the anomaly occurs.

[0163] More specifically, in practical applications, the intensity of the laser scattering signal can be collected in real time by a laser sensor, and the concentration of particulate matter can be indirectly measured by a particulate matter sensor or the laser scattering method. For example, when it is monitored that the intensity of the laser scattering signal increases from 1000 units to 1200 units, and the concentration of particulate matter increases from 50 μg / m³ to 60 μg / m³, the change coefficient of the dust concentration can be calculated as . This value can be used to judge the degree of change in the dust concentration and the possible types of anomalies.

[0164] Preferably, in order to improve the calculation accuracy of the change coefficient of the dust concentration, the method of multi-point averaging can be adopted. That is, multiple monitoring points are set in the monitoring area, the intensity of the laser scattering signal and the particulate matter concentration data of each point are collected respectively, then the change coefficient of the dust concentration of each point is calculated, and finally the average value is taken as the change coefficient of the dust concentration of the entire monitoring area.

[0165] Furthermore, the concept of time weighting can also be considered, and the change coefficient is weighted according to the time interval before and after the occurrence of the anomaly. For example, if the time interval before and after the occurrence of the anomaly is short, a larger weight can be given to highlight the rapid change of the dust concentration in a short time. Through these refined operation steps and alternative solutions, the calculation accuracy and reliability of the change coefficient of the dust concentration can be further improved, providing more reliable data support for dust monitoring and management.

[0166] Next, refer to Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal devices shown are only examples and should not impose any limitations on the functions and usage ranges of the embodiments of the present invention.

[0167] As shown in Figure 3As shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0168] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wireline to exchange data. Although Figure 3 an electronic device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had. Figure 3 Each block shown in may represent a device or, as needed, multiple devices.

[0169] Furthermore, the storage medium of the embodiments of the present application stores program instructions capable of implementing all the above methods. Among them, the program instructions may be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media capable of storing program codes such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0170] The above description is only some preferred embodiments of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the embodiments of the present invention that have similar functions.

Claims

1. An online dust detection method based on laser, characterized in that, It includes the following steps: When the dust concentration in the dust monitoring area is detected to be abnormal, obtain the laser scattering signal intensity and particulate matter concentration in the monitoring area before and after the abnormality occurs; Based on the obtained information, calculate the laser scattering energy and the change rate of particulate matter concentration in the monitoring area, and then obtain the dust concentration change coefficient in the monitoring area; Among them, the calculation formula for laser scattering energy is: In the formula, is the laser scattering signal intensity that changes with time in the monitoring area, is the monitoring period; According to the dust concentration change coefficient and the dust concentration abnormality identification criterion in the monitoring area, judge the dust concentration abnormality type. If the abnormality type is that the dust concentration in the area exceeds the standard, start the warning action to realize the management of the dust monitoring area; The dust concentration change coefficient in the monitoring area is expressed as: In the formula, represents the coefficient of change in dust concentration; , respectively represent the laser scattering signal intensities before and after the occurrence of the anomaly; , respectively represent the particulate matter concentrations before and after the occurrence of the anomaly.

2. The on-line dust detection method based on laser according to claim 1, characterized in that, The dust concentration abnormality identification criterion includes: If it is satisfied in the monitoring area that the dust concentration change coefficient is greater than the first set threshold and the change rate of particulate matter concentration is greater than the second set threshold, it is judged as an event that the dust concentration in the area exceeds the standard; If it is satisfied in the monitoring area that the dust concentration change coefficient is less than or equal to the first set threshold, but the change rate of particulate matter concentration is greater than the second set threshold, it is judged as a dust diffusion event; If it is satisfied in the monitoring area that the dust concentration change coefficient is greater than the first set threshold, but the change rate of particulate matter concentration is less than or equal to the second set threshold, it is judged as a dust source release event; If it is satisfied in the monitoring area that the dust concentration change coefficient is less than or equal to the first set threshold and the change rate of particulate matter concentration is less than or equal to the second set threshold at the same time, it is judged as normal environmental fluctuation.

3. The on-line dust detection method based on laser according to claim 1, characterized in that, Determine the first set threshold and the second set threshold in the monitoring area in the following way: First set threshold = ; Second set threshold = ; In the formula, represents the average dust concentration change coefficient of the monitoring area under normal environmental conditions, represents the maximum dust concentration change coefficient of the monitoring area under extreme environmental conditions; represents the average particulate matter concentration change rate of the monitoring area under normal environmental conditions, represents the maximum particulate matter concentration change rate of the monitoring area under extreme environmental conditions; The , , , acquisition method is as follows: Through statistical analysis of historical monitoring data, calculate the average and maximum dust concentration change coefficients and the change rates of particulate matter concentration in the monitoring area under normal and extreme environmental conditions respectively.

4. The on-line dust detection method based on laser according to claim 3, characterized in that The maximum dust concentration change coefficient in the monitored area under extreme environmental conditions , expressed as: In the formula, and respectively represent the maximum and minimum laser scattering signal intensities in the monitoring area under extreme environmental conditions; and respectively represent the maximum and minimum particulate matter concentrations in the monitoring area under extreme environmental conditions.

5. The on-line dust detection method based on laser according to claim 4, characterized in that The maximum laser scattering signal intensity within the monitored area , is expressed as: Wherein, is the laser scattering signal intensity varying with time in the monitoring area, is the Dirac function, is the time point when the laser scattering signal intensity reaches the maximum value under extreme environmental conditions, is the monitoring period.

6. The on-line dust detection method based on laser according to claim 5, wherein, The maximum particulate matter concentration within the monitored area , is expressed as: In the formula, is the particulate matter concentration that changes with time in the monitoring area, is the Dirac function, is the time point when the particulate matter concentration reaches the maximum under extreme environmental conditions, is the monitoring period.

7. The online dust detection method based on laser according to claim 6, characterized in that, The change rate of the particulate matter concentration within the monitored area , is expressed as: In the formula, is the particulate matter concentration at the current moment in the monitoring area, is the particulate matter concentration at the next moment in the monitoring area, is the time interval.

8. An on-line dust detection system based on laser, characterized in that It includes: A data acquisition module, which is used to obtain the laser scattering signal intensity and particulate matter concentration in the monitoring area before and after the abnormality occurs when the dust concentration in the dust monitoring area is detected to be abnormal; A laser scattering energy calculation module, which is used to calculate the laser scattering energy according to the obtained laser scattering signal intensity, and the calculation formula is: In the formula, is the laser scattering signal intensity that changes with time in the monitoring area, is the monitoring period; A dust concentration change coefficient calculation module, which is used to calculate the laser scattering energy and the change rate of particulate matter concentration in the monitoring area based on the obtained information, and then obtain the dust concentration change coefficient in the monitoring area; A dust concentration abnormality identification and warning action module, which is used to judge the dust concentration abnormality type according to the dust concentration change coefficient and the dust concentration abnormality identification criterion in the monitoring area. If the abnormality type is that the dust concentration in the area exceeds the standard, start the warning action to realize the management of the dust monitoring area; The dust concentration change coefficient in the monitoring area in the dust concentration abnormality identification and warning action module is expressed as: In the formula, represents the coefficient of change in dust concentration; , respectively represent the laser scattering signal intensities before and after the occurrence of an anomaly; , respectively represent the particulate matter concentrations before and after the occurrence of an anomaly.

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